Detecting changes in surface moisture and water table position with spectral changes in surface vegetation in northern peatlands

Due to warmer and drier conditions, wildland fire has been increasing in extent into peatland ecosystems during recent decades. As such, there is an increasing need for broadly applicable tools to detect surface peat moisture, in order to ascertain the susceptibility of peat burning, and the vulnerability of deep peat consumption in the event of a wildfire. In this thesis, a field portable spectroradiometer was used to measure surface reflectance of two Sphagnum moss dominated peatlands. Relationships were developed correlating spectral indices to surface moisture as well as water table position. Spectral convolutions were also applied to the high resolution spectra to represent spectral sensitivity of earth observing sensors. Band ratios previously used to monitor surface moisture with these sensors were assessed. Strong relationships to surface moisture and water table position are evident for both the narrowband indices as well as broadened indices. This study also found a dependence of certain spectral relationships on changes in vegetation cover by leveraging an experimental vegetation manipulation. Results indicate broadened indices employing the 1450-1650 nm region may be less stable under changing vegetation cover than those located in the 1200 nm region.

Evaluating the usefulness of soil moisture (forecasts) in streamflow prediction for central Texas

The past decade has brought significant advancements in seasonal climate forecasting. However, water resources decision support and management continues to be based almost entirely on historical observations and does not take advantage of climate forecasts. This study builds on previous work that conditioned streamflow ensemble forecasts on observable climate indicators, such as the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) for use in a decision support model for the Highland Lakes multi-reservoir system in central Texas operated by the Lower Colorado River Authority (LCRA). In the current study, seasonal soil moisture is explored as a climate indicator and predictor of annual streamflow for the LCRA region. The main purpose of this study is to evaluate the correlation of fractional soil moisture with streamflow using the 1950-2000 Variable Infiltration Capacity (VIC) Retrospective Land Surface Data Set over the LCRA region. Correlations were determined by examining different annual and seasonal combinations of VIC modeled fractional soil moisture and observed streamflow. The applicability of the VIC Retrospective Land Surface Data Set as a data source for this study is tested along with establishing and analyzing patterns of climatology for the watershed study area using the selected data source (VIC model) and historical data. Correlation results showed potential for the use of soil moisture as a predictor of streamflow over the LCRA region. This was evident by the good correlations found between seasonal soil moisture and seasonal streamflow during coincident seasons as well as between seasonal and annual soil moisture with annual streamflow during coincident years. With the findings of good correlation between seasonal soil moisture from the VIC Retrospective Land Surface Data Set with observed annual streamflow presented in this study, future research would evaluate the application of NOAA Climate Prediction Center (CPC) forecasts of soil moisture in predicting annual streamflow for use in the decision support model for the LCRA.